1. Field of the Invention
The invention is related to an apparatus and method for sorting objects, in particular fruit, by color and shape and for compensating for errors in such sorting systems.
2. Related Art
Numerous attempts have been made to sort items, such as fruit, by color. U.S. Pat. No. 2,881,919 to Bartlett discloses the use of multiple photocells to determine the intensity of light measured from discrete and focused areas of a peach. U.S. Pat. Nos. 3,066,797, 4,454,029, and 3,993,899 disclose sorting machines which use fiber optics to sense different portions of an object and which use light sensors which sense different colors. U.S. Pat. No. 3,770,111 discloses an apple sorter which includes numerous fiber optic cables located around the circumference of an apple. The fiber optic cables are routed to two different color sensors. U.S. Pat. Re 29,031 discloses a circuit for sorting apples according to a ratio of colors. U.S. Pat. Nos. 4,057,146 and 4,132,314 disclose sorters which use fiber optic cables and a ratio of colors to sort fruit into two or several color categories. These sorters use photosensitive devices and do not compute the percentage of a certain color.
Vartec Corp. markets an optical inspection system known as Megaspector which uses an image processor implementing gray-scale processing methods. The Vartec processor inspects each individual item in the field of view and determines its acceptability based on user programmed inspection criteria. An article entitled High Speed Machine Vision Inspection for Surface Flaws Textures and Contours by Robert Thomason discloses a system employing an algorithm that processes neighborhood gray-scale values and vector values as implemented in circuit hardware in a distributed processing computer. Thomason discloses that in gray-scale and neighborhood processing techniques, each pixel has a numeric value (64 levels for 6-bit, 256 levels for 8-bit) which represents its gray-scale value. The neighborhood processing compares a pixel with its neighbors and filters out irrelevant information. This transforms each image into another image that highlights desired information. Using low pass filtering, signal to noise ratio can be improved, while high pass filtering enhances the edges of an image. Thomason further discloses a method in which the images are analyzed by high pass filtering to highlight edges and contours and by vector direction at each pixel in order to distinguish edge features from defects on the surface of an object. Pixels in the image are compared to a preprogrammed look-up table, which contains patterns associated with each type of feature.
Automated Inspection/Classification of Fruits and Vegetables by William Miller in The Transactions of the 1987 Citrus Engineering Conference discloses grading requirements and sensor techniques for various sorting approaches. FIG. 3 provides response curves for various optical detectors and FIG. 6 discloses general schematics for different sorting systems.
Automated machine Vision Inspection of Potatoes by Y. Tao, et al. published in 1990 discloses a machine vision system for inspecting potatoes by size, color, shape and blemishes. The system employed methods of using HSI (hue, saturation, and intensity) color scheme and multi-variant discriminate analysis for potato greening classification. Tao discloses a color transformation which reduces color evaluation for red, green and blue stored in three image buffers to one single hue buffer. Hue, H, is calculated by: EQU H=[90.degree.+tan.sup.-1 ([2R-G-B]/[.sqroot.3(G-B)])+180.degree. if G&lt;B]]*255/360 Eqn 1
Tao further discloses that color feature extraction was achieved using a hue histogram which gathers color components and the amount of area of the color in an image. A blue background was used for best contrast between the potato and the background. Tao discloses that it was necessary to use a multi-variant discriminate method for potato classification, since it was difficult to determine a single effective threshold for greening determination. A linear discriminate function was also generated in which the primary procedure was to train the program by samples for the classification criteria and classify a new sample based on the criteria.
Other conventional approaches require obtaining a red-to-green ratio or a mixture of red, green and blue ratios. Clustering, red, green and blue variations, cut by color groups, and trend analysis for grading have also been employed.
U.S. Pat. No. 5,159,185 to Lehr discloses a lighting control system for maintaining a light source and measuring components of a color measurement station in a stabilized condition. A video camera simultaneously measures a test sample and a standard color tile. The system relies on adjusting the lighting by adjusting a fluorescent lamp drive until one of the signals from the standard tile portion of the signal is within a prescribed variation from a reference stored in memory. At that time the test sample is evaluated.
Many of the above color sorters have been of limited use because they require the operator to identify percentages or other measures of individual colors for sorting purposes. Such methods introduce significant complexity and related errors. The method taught by Tao does not disclose a system which provides an operator the ability to establish separate grading criteria.